Measuring Exposure to the Credit Shock

Description of Firm-Level Data in Amadeus

All the firm-level information we are exploiting comes from Amadeus. The database is one of the products in Bureau van Dijk’s Osiris – together with Bankscope, Isis and Orbis – and provides comprehensive information for approximately 19 million companies in both Western and Eastern Europe. It is not a historical database as it strives for recent information. Financial data from companies within Amadeus are retained fro a rolling period of 8 years. When a new year of data is added, the oldest set of data is dropped, meaning only the most recent data for each company is available. Banks and financial companies are generally excluded from the database, such that the reminder is only commercial companies. Companies are generally excluded from the database, such that the reminder is only commercial companies. Companies are defined in different categories by their operating revenue, total assets, and number of employees: small, medium, large and very large, handled in separate tables of the database. In addition to financial data, the database provides information cross-sectional information about the bankers the company has relationships with. Those bank relationships are crucial for the measure of the credit shock we are studying, proxied by the county-level Commerbank dependence. To cite these data, we can use the following format: Wharton Research Data Services. “WRDS” wrds.wharton.upenn.edu, accessed X.

From Kalemli-Ozcan et al. (2015) we understand some ex ante limitations of the Amadeus database. It will delete a company from the database if the company did not report anything in the last 5 years, contrary to Orbis that keeps the company as long as it is active in the business register. It would lead to a survivorship bias in our underlying firm-level data, meaning that we can achieve a better representative sample combining both Amadeus and Orbis. There is a reporting lag of two years, on average, and there are differences in the coverage of particular variables depending on when the BvD product has been released. Hence, for the 2010 vintage, the company may not have the 2010 filings but the 2010 filings would appear in the 2012 vintage. For our purpose, it means that we might look into financials until 2008 when referring to 2006 financials. Moreover, BvD’s data collection improves over time and hence this lag might vary by country and data vintage.

Additionally, it can be possible that there are multiple financial records for a single closing data due to double reporting of both consolidated and unconsolidated financial variables. There is no perfect solution in using only consolidated or unconsolidated accounts. However, it is possible to identify the consolidation type from the variables repbas in the financials table, and consol in the company information, which can be:

  • C1: only consolidated accounts,
  • U1: only unconsolidated accounts,
  • C2: both consolidated and unconsolidated, and those coded as C2 are the consolidated ones,
  • U2: both consolidated and unconsolidated, and those coded as U2 are the unconsolidated ones,
  • LF: limited financial information.

After talking with Kilian Huber, we understand that the original data on bank relationships in his paper from Creditreform are not available anymore for research purposes, so we have no other option than to use Amadeus data. The plan is to check later whether our measure is highly correlated to his measure, both at firm- and county-level, or good enough to use it as a proxy of the correct measure of Commerzbank dependence. The same procedure applies in Berg et al. (2021), where they analyse spillovers effects in corporate finance using Amadeus bank relationships and financial data at 2007. It relies on the assumption of long-term stickiness of bank relationships, well documented in Chodorow-Reich (2014) as in Kilian’s and Tobias’ papers.

Data Extraction using the Wharton Research Data Services (WRDS)

Our connection to the Amadeus database is provided by the Wharton Research Data Services, via our universities’ subscription. We need to set up the handshaking to the database in R using PostgreSQL driver. In the older version of my code in Stata, we were using the ODBC driver straight into Stata.

# this is initializes the database connection with the WRDS,
# it can also go in the project's .Rprofile config.
wrds <- RPostgres::dbConnect(Postgres(),
                             host = "wrds-pgdata.wharton.upenn.edu",
                             port = 9737,
                             dbname = "wrds",
                             sslmode = "require",
                             user = "alepizzigolotto")

For the data extraction we are interested in three tables of the database, which are unique tables for each firm size category.

  • amadeus_x table contains information on company profiles. Among all the variables available, we extract the ID idnr and the name name of the company, the zipcode of the location of the factory, together with the address, region – which indicates the landkreis – and city, the incorporation date and year dateinc, the legal status lstatus and when the status has changed, the NACE, NAICS and SIC primary codes (nace_prim_code, naics_core_code and ussic_core_code). We retrieve the firm size flag ftype from the table which we interrogate in the database.
  • financials_x table contains the unbalanced panel of financial data for each firm kept in the sample at the time of interrogation. We are using those information to understand more about the firm balance sheets, grouping at county level and by firm size flag, using some of those information to weight the county-level Commerzbank dependence at firm level. We retrieve the number of employees empl, total assets toas, current loans loan, long-term debt ltdb, cost of employees staf - from which we can obtain the average wage -, shareholders funds and liabilities tshf (right-hand side of balance sheet), shareholders funds shfd (equity), liquidity ratio liqr, solvency ratio solr, current assets cuas, current liabilities culi, working capital wkca, and operating revenue opre (and more in the database).
  • bankers_x table contains cross-sectional information about each firm’s bank relationships, where the only information available is the name of the bank bnk_name, from which we can identify which relationships are with Commerzbank and which are not.
  • (optional) subsidiaries_x table contains among all companies in Amadeus the information relative subsidiaries within the database, with their name subs_name, their id number subs_bvdepnr, the city subs_city, status subs_status, date of information subs_date, the total assets subs_toas and number of employees subs_empl, and the closing date subs_clos.

With our function getAmadeusData(db, table, vars, type, cond) we can retrieve data from the Amadeus database via the db pre-initialized connection object to WRDS PosgreSQL server, the selected table table, the firm size flag type and some conditions for the SQL query as arguments. We collect all data for all firms from Germany that are in Amadeus in a list with the following script.

🛑 FIXME put this part of the code and the entire script in a separate script for the pipeline!

# same condition for everybody and then we merge later using the
# incorporation year to filter only the firms active before the 2007
cond <- "WHERE country = 'GERMANY'"
# firm types in Amadeus
firm_types <- c("v", "l", "m", "s")
# company info, financials, bankers
tables <- c("amadeus", "financials", "bankers", "subsidiaries")
vars <- list(company_vars, financials_vars, bankers_vars, subsidiaries_vars)
amadeus_data <- list()
for (t in 1:length(tables)) {
    amadeus_data[[t]] <- list()
    for (s in 1:length(firm_types)) {
        amadeus_data[[t]][[s]] <- getAmadeusData(
            wrds, tables[[t]], vars[[t]], firm_types[[s]], cond)
    }
    amadeus_data[[t]] <- rbindlist(amadeus_data[[t]])
}

We save the data in four separate .fst files to be able to reuse them. I have disabled the code here because I have already exported all data from Amadeus in the separate files to work locally without connecting to the database. You can find the last update in the README.md file in the ROOT/data/firms directory.

The raw data consists of a total of 1516363 unique companies of which we have 8176154 unbalanced financial entries, from virtually 1952 to 2022, and 1263901 cross-sectional bank relationships.

Each Firm Belongs to His County: Matching Procedure

The company profile data in Amadeus provide us with different information to perform the matching with the county of their location. In order of relevance, the first information we get is the ZIP code, then we have their region, their city, and eventually their address. This order is given by the quality of the information available, as information can be missing, not updated, or misleading, whereas the ZIP code is pretty stable.

Table 1: View of the extracted company profile information from Amadeus including the company code, the original name, ZIP code, address, region and city.
idnr name_nat zipcode address_nat region_nat city_nat
DE7050297176 AGENNIX AG 69120 IM NEUENHEIMER FELD 515 Heidelberg HEIDELBERG
DE2010000074 PSI SOFTWARE AG 10178 DIRCKSENSTR. 42-44 Berlin BERLIN
DE2010000230 BERGMANN & FRANZ NACHF. GMBH & CO. KG 10785 LÜTZOWSTR. 74-76 Berlin BERLIN
DE2010000474 LAEGER GMBH 12347 TEMPELHOFER WEG 50 Berlin BERLIN
DE2010000478 KIEBACK & PETER GMBH & CO. KG 12347 TEMPELHOFER WEG 50 Berlin BERLIN
DE2010000524 PELIKAN GROUP GMBH 13507 AM BORSIGTURM 100 Havelland BERLIN
DE2010000564 OTIS GMBH & CO. OHG 13507 OTISSTR. 33 Berlin BERLIN
DE2010000581 SIEMENS AKTIENGESELLSCHAFT 13629 NONNENDAMMALLEE 101-104 Berlin BERLIN
DE2010000805 DEGEWO AKTIENGESELLSCHAFT 10785 POTSDAMER STR. 60 Berlin BERLIN
DE2010001043 SPIELE MAX GMBH 12249 HAYNAUER STR. 72 A Postdam-Mittelmark BERLIN

ZIP Codes – County Name – Village Name – County Codes Matching Table

We use data from the Postleitzahlen Deutschland, which provides an accurate matching table between ZIP codes and KKZ codes, the county identifiers that are also used in the German Socio-Economic Panel. From these data, we obtain for each ZIP code plz the corresponding county code ags, the Landkreis name landkreis (if it is a landkreis), the Bundesland name and the location name ort. We keep KKZ at county level (five digits).

# load from website the .csv file with the matches
kreise_codes <- read.csv(
    url(glue::glue(
        "https://www.suche-postleitzahl.org/",
        "download_files/public/zuordnung_plz_ort_landkreis.csv"))
    ) %>%
    as.data.table()
# keep KKZ codes at county level in five digits
kreise_codes[, kkz := str_sub(
    fifelse(str_length(ags) == 7, paste0("0", ags), as.character(ags)), 1, 5)]
# in the .do file we put Landkreis and Kreis at the end of the string
# we do it here as well and then let's see
kreise_codes[, names := fifelse(
    str_trim(landkreis) == "", paste0(ort, " Stadt"),
    str_replace(landkreis,"(Landkreis|Kreis|Eifelkreis)\\s(.+)", "\\2 \\1"))]
Table 2: View of the ZIP Code – County Code – Landkreis Name – Village Name Table coming from the Postleitzahlen Deutschland website.
idnr name_nat zipcode address_nat region_nat city_nat
DE7050297176 AGENNIX AG 69120 IM NEUENHEIMER FELD 515 Heidelberg HEIDELBERG
DE2010000074 PSI SOFTWARE AG 10178 DIRCKSENSTR. 42-44 Berlin BERLIN
DE2010000230 BERGMANN & FRANZ NACHF. GMBH & CO. KG 10785 LÜTZOWSTR. 74-76 Berlin BERLIN
DE2010000474 LAEGER GMBH 12347 TEMPELHOFER WEG 50 Berlin BERLIN
DE2010000478 KIEBACK & PETER GMBH & CO. KG 12347 TEMPELHOFER WEG 50 Berlin BERLIN
DE2010000524 PELIKAN GROUP GMBH 13507 AM BORSIGTURM 100 Havelland BERLIN
DE2010000564 OTIS GMBH & CO. OHG 13507 OTISSTR. 33 Berlin BERLIN
DE2010000581 SIEMENS AKTIENGESELLSCHAFT 13629 NONNENDAMMALLEE 101-104 Berlin BERLIN
DE2010000805 DEGEWO AKTIENGESELLSCHAFT 10785 POTSDAMER STR. 60 Berlin BERLIN
DE2010001043 SPIELE MAX GMBH 12249 HAYNAUER STR. 72 A Postdam-Mittelmark BERLIN

First Step: ZIP Code Matching

First, we try to match all companies with their county code using the ZIP codes indicated in the Amadeus database.

# matching table zip-kkz keeping unique values in the previous data
match_zips <- unique(kreise_codes[, .(plz, kkz)])
# we keep zip code as integer
companies[, zipcode := as.integer(zipcode)]
# double check that Landkreis and Kreis are at the end of the 
# A[B, on = 'a', bb := i.b], join A with B and upddate A by reference
companies[match_zips, on = .(zipcode = plz), kkz := i.kkz]

Second Step: String Fuzzy Matching with County Names

After matching ZIP codes with the information in the Amadeus’ companies, we are left with 26994 unmatched firms. For most of them (26269) we have information of the region where those firms are located. We can use UTF-8 original names for the match, contrary to Stata that messes up all the time with strings. After some adjustments of the string names, we run a Jaro-Winkler distance algorithm to fuzzy matching the region name of firms without a match with the ZIP code (or missing ZIP code) with the region names in the matching table, specified in Landkreis for most of all the rural areas and Stadt for most of all the urban areas. We select for each unmatched companies’ region name the county name with the smallest distance in the matching table. The Jaro-Winkler distance is not perfect, so we need to perform some manual changes to fit the names after the fuzzy matching procedure.

# reverse epithets of counties just in case
companies[, region_nat := str_replace(
  region_nat,"(Landkreis|Kreis|Eifelkreis)\\s(.+)", "\\2 \\1")]
# select from the matching table only names and county codes
match_names <- unique(kreise_codes[, .(kkz, names)])
# select only the region names for unmatched firms in the step before
unmatched_regions <- data.table(
    names = unique(companies[is.na(kkz) & !is.na(region_nat), region_nat]))
# create grid for matching each region name with names in the matching table
scores <- expand.grid(unmatched_regions[, names], match_names[, names])
# run Jaro-Winkler string distance function
scores$dist <- stringdist::stringdist(scores$Var1, scores$Var2, method = "jw")
colnames(scores) <- c("region_nat", "names", "dist")
# there are some region names that are Bundesland names and we remove it
states <- unique(kreise_codes[bundesland != "Berlin", bundesland])
# select the name with the smallest distance for each region name
match_names <- scores %>%
    dplyr::filter(!(region_nat %in% states)) %>%
    dplyr::group_by(region_nat) %>%
    dplyr::arrange(dist) %>%
    dplyr::slice(1) %>%
    dplyr::select(region_nat, names) %>%
    as.data.table()
# add county codes back from the matching table
match_names[kreise_codes, on = .(names), kkz := i.kkz]
# manual corrections
match_names[region_nat %like% "München  Landeshaupt", names := "München Stadt"]
match_names[region_nat %like% "Lauenburg", names := "Herzogtum Lauenburg Kreis"]
match_names[region_nat %like% "Neuss", names := "Rhein-Neuss Kreis"]
match_names[region_nat %like% "Wendel", names := "St. Wendel Landkreis"]
# add county codes to the unmatched companies
companies[
    match_names, on = .(region_nat), kkz := fifelse(is.na(kkz), i.kkz, kkz)]

Third Step: String Fuzzy Matching with “City” Names

After using strings with region names for the match of companies with their county code, we still have 1107 unmatched firms. Of those firms, 925 have information about the city where the firm is located. First, we search for perfect match of of companies’ city names that are unique at county level in the matching table for Germany as a whole. Second, we use the Jaro-Winkler distance again combining information on the city with the Bundesland in the matching table to assign county codes for the remaining cities.

# select the city of firms that are left unmatched
unmatched_cities <- unique(
  companies[is.na(kkz) & !is.na(city_nat), c("city_nat", "region_nat")])
# select cities by couinty codes and their Bundesland
match_cities <- unique(kreise_codes[, .(kkz, ort, bundesland)])
# count towns with the same name for an indicator
match_cities[, n_cities := .N, by = .(ort)][, city_nat := toupper(ort)]
# exact match cities in the unmatched firms, when cities has unique name
unmatched_cities[
    match_cities[n_cities == 1, .(kkz, city_nat)],
    on = .(city_nat), kkz := i.kkz]

# fuzzy matching for remaining cities using bundesland names
match_cities[, names := paste0(city_nat, " - ", toupper(bundesland))]
scores <- expand.grid(
  unique(unmatched_cities[is.na(kkz), city_nat]), match_cities[, names])
scores$dist <- stringdist::stringdist(scores$Var1, scores$Var2, method = "jw")
colnames(scores) <- c("city_nat", "names", "dist")
scores <- scores %>%
    dplyr::group_by(city_nat) %>%
    dplyr::arrange(dist) %>%
    dplyr::slice(1) %>%
    dplyr::select(city_nat, names) %>%
    as.data.table()
# manual correction of bad matching
nuisance_cities <- c("SENSBACHTAL", "RIEZLERN", "HIRSCHEGG", "TWIEFLINGEN",
                     "HOYERSHAUSEN", "ZÖBLITZ", "WIES", "NEUNKIRCHEN",
                     "HELSINKI", "SACHSEN", "KUOPIO", "TAMPERE")
scores <- scores[!(city_nat %in% nuisance_cities)]
# manual correction city mismatch
city_mismatch <- c("LICHTE", "HADMERSLEBEN", "BRANDENBURG", "MARL", "VOERDE",
                   "FREIBURG", "LUDWIGSHAFEN", "ST. WENDEL", "MUNICH")
city_correction <- c("Sonneberg - Thüringen", "Oschersleben - Sachsen-Anhalt",
                     "Brandenburg an der Havel - Brandenburg",
                     "Marl - Nordrhein-Westfalen",
                     "Voerde (Niederrhein) - Nordrhein-Westfalen",
                     "Freiburg im Breisgau - Baden-Württemberg",
                     "Ludwigshafen am Rhein - Rheinland-Pfalz",
                     "Sankt Wendel - Saarland", "München - Bayern")
for (s in 1:length(city_mismatch)) {
    scores[city_nat == city_mismatch[s], names := toupper(city_correction[s])]
}

scores <- merge(scores, match_cities[, .(names, kkz)], by = "names")
unmatched_cities[
  scores[, .(kkz, city_nat)], on = .(city_nat),
  kkz := fifelse(is.na(kkz), i.kkz, kkz)]
unmatched_cities <- unique(unmatched_cities[!is.na(kkz), .(kkz, city_nat)])
# add county codes to the unmatched companies
companies[unmatched_cities, on = .(city_nat),
          kkz := fifelse(is.na(kkz), i.kkz, kkz)]

Unfortunately, we have to manually correct some of the cities and, after merging back to the companies table, we have been able to match all but 227 firms, with a total of 1516136 firms in the sample.

Fourth-Step: Harmonize county codes with the official German Socio-Economic Panel Recoded County Codes.

The last operation to do is to match the county codes we have matched in the Amadeus sample with the county level codes contained in the German Socio-Economic Panel. In fact, the SOEP with county identifiers we access in the SOEPRemote has re-coded county codes keeping track of counties that have been merged at different points in time between 2000 and 2016, but never the inverse process. We have retrieved a table from the internal server at DIW Berlin that contains the matches between KKZ and KKZ_REK, namely the standard codes and the re-coded ones.

We convert the county codes from string to numeric (as some of them they keep a zero in the front, which is not the case for the SOEPRemote codes), and we perform the match using the integer codes.

Firm Selection and Bank Relationships

After matching the universe of firms to their county in the Amadeus database, we subset for those firms that were established before 2007 for which we have information of bank relationships. This is our way to identify firms active on the eve of the credit shock happening in 2008Q3, and the year at which Huber (2018) collects the bank relationships. After filtering, we count the number of bank relationships for each firm, and how many of those relationships are with Commerzbank using string matching on the name, and we assign back the values to our firms. The row bankers table looks as the following before subsetting information for the firms we are interested of.

Table 3: View of the extracted company bank relationships from Amadeus.
bnk_name compcat country idnr ftype
Commerzbank AG VERY LARGE GERMANY DE2010000564 V
Berliner Sparkasse Ndl. der Landesbank Berlin AG VERY LARGE GERMANY DE2010000074 V
Commerzbank AG VERY LARGE GERMANY DE2010000074 V
Deutsche Postbank AG VERY LARGE GERMANY DE2010000074 V
HypoVereinsbank UniCredit Bank AG VERY LARGE GERMANY DE2010000074 V
Commerzbank AG VERY LARGE GERMANY DE2010000230 V
Deutsche Postbank AG VERY LARGE GERMANY DE2010000230 V
Berliner Bank NL der Dt. Bank Priv.-u.Geschäftsk. AG VERY LARGE GERMANY DE2010000474 V
Commerzbank AG VERY LARGE GERMANY DE2010000474 V
Berliner Bank NL der Dt. Bank Priv.-u.Geschäftsk. AG VERY LARGE GERMANY DE2010000478 V

Filtering firms established before 2007 leaves us with a total of 631933 firms. Merging the bank relationships with this sub-sample of firms makes us losing 78477 firms, of which we have no information connected abbout the bank accounts, remaining with a total of 553456. We obtain a total of , of which are with Commerzbank (10.08%). We use the strings in bnk_name to identify Commerzbank. 84.74% of firms have no accounts with Commerzbank (which means that around 15% of the firms have a relationship with Commerzbank, roughly 10 percentage points less than the relationships in Huber’s paper). This is the first step for the creation of the measure of Commerzbank dependence, and the baseline for the firm-level measure.

The following table summarizes for the firm sample the distribution of bank relationships across different firm size flags and the number of firms per county in each category. It is interesting to see that the largest part of our firm sample is dominated by small and medium firms with few bank relationships and fewer with Commerzbank. The median number of bank relationships is one or two, independently from the firm size flag. The histograms give the flavour of the distribution of the frequency of firms across firm type categories and in total.

Table 4: Summary Statistics of firms from Amadeus which were established before 2007 and have signaled bank relationships in the database by firm size.
Variable Firm Size Overall, N = 553456
Large, N = 25996 Medium, N = 166440 Small, N = 353747 Very Large, N = 7273
No. of Bank Relationships
N missing (% missing) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0)
Mean 1.996 1.696 1.377 2.305 1.514
SD 1.121 0.837 0.641 1.452 0.775
Minimum 1 1 1 1 1
Median (IQR) 2 (1 - 3) 2 (1 - 2) 1 (1 - 2) 2 (1 - 3) 1 (1 - 2)
Maximum 15 11 11 15 15
No. of Relationships with Commerzbank
N missing (% missing) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0)
Mean 0.28 0.174 0.128 0.423 0.153
SD 0.449 0.379 0.334 0.498 0.36
Minimum 0 0 0 0 0
Median (IQR) 0 (0 - 1) 0 (0 - 0) 0 (0 - 0) 0 (0 - 1) 0 (0 - 0)
Maximum 2 2 2 4 4
Number of Firms per County
N missing (% missing) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0)
Mean 64.99 416.1 884.367 18.183 345.91
SD 95.602 484.803 1276.452 34.667 766.922
Minimum 0 0 2 0 0
Median (IQR) 42 (27.75 - 71.25) 299 (192.75 - 489.25) 575 (376 - 952.75) 9.5 (5 - 18) 129.5 (23 - 416)
Maximum 1104 6202 16568 377 16568
Histogram of the share of total number of firms per county by size   category (top) and in total (bottom).Histogram of the share of total number of firms per county by size   category (top) and in total (bottom).

Figure 1: Histogram of the share of total number of firms per county by size category (top) and in total (bottom).

Using the bank relationships from Amadeus, we construct a simple measure of firm-level Commerzbank dependence exactly following Huber (2018). We obtain a slightly different distribution compared to the figure in his paper. It is probably because we are considering way more firms compared to his, with lower number of relationship banks per firm and more firms without any relationship with Commerzbank. It might be related to a higher presence of small and medium firms.

Histogram of the fraction of firms in the sample at a certain number   of bank relationships in total and only with Commerzbank.

Figure 2: Histogram of the fraction of firms in the sample at a certain number of bank relationships in total and only with Commerzbank.

Distribution of firm-level Commerzbank dependence conditional and   unconditional on having at least one bank relationship with   Commerzbank in the sample.

Figure 3: Distribution of firm-level Commerzbank dependence conditional and unconditional on having at least one bank relationship with Commerzbank in the sample.

Histogram of firm-level Commerzbank dependence as calculated in Huber   (2018) from his firm sample.

Figure 4: Histogram of firm-level Commerzbank dependence as calculated in Huber (2018) from his firm sample.

In the initial version of the draft for this project, we followed a similar structure for retrieving the firm data, matching them with their county codes and subset the firms established before 2007 and of which we have the bank relationships. The main difference was that we were matching after the first step (ZIP codes) with a more manual procedure using regional names. From that time - around June-July 2020 - some of the firms or bank relationships have been dropped from the database, and we did not save the full information of those firms but just the bank relationships, the identifiers and the four-digits industry code. We can compare the distribution of the firm-level Commerzbank dependence of those firms with the distribution in this sample we have recently retrieved.

In the past firm sample, we have 70827 additional firms. It seems to be caused by a combination of firms that were dropped in the whole Amadeus database and updated to missing information in the bankers table. Trying to find the firms we previously retrieved in this sample leaves us with only 549754. When it comes to the firm-level Commerzbank dependence distribution from the previous draft, it is very similar to the one we obtain with the current firms.

Distribution of firm-level Commerzbank dependence conditional and   unconditional on having at least one bank relationship with   Commerzbank, using the firm data from the previous draft with the bankers   we retrieved at that time (Stata code).

Figure 5: Distribution of firm-level Commerzbank dependence conditional and unconditional on having at least one bank relationship with Commerzbank, using the firm data from the previous draft with the bankers we retrieved at that time (Stata code).

Firm-Level Weights for County Level Exposure

In this section, we go through the weighting procedure for calculating the county-level Commerzbank dependence. Ideally, we want to take care of all the within-county heterogeneity among firms that cannot be controlled by county-level fixed effects when applying our main identification. Firms are clustered over two main dimensions when considering within-county variation, which are the firm size, divided in four categories, and industry sector. From previous section, firms with different size category do not present striking differences in the number of bank relationships and the number of those with Commerzbank, but can have different sensibility to a general credit shock in terms of liquidity availability. Moreover, we do not exactly know what means to be a firm of size X. On the other hand, firms within the same industry can present similar characteristics, especially when they are within the same regional boundaries.

Two-Digits Industry Sectors in the Firm Sample

From the companies profile, for each firm we have information of the four digits NACE v2 industry code. We group firms using the two-digit specification of industry sector to see the number of firms in the sample after restricting by incorporation date and presence of bank relationships for each group. There are 18 firms for which the industry code is not available. Industry composition within a county should be taken care by the county-level fixed effects in our identification.

Table 5: NACE rev. 2 two-digits codes with description and number of firms in our sample for all firms established before 2007 of which we have the bank relationships, in order of frequency.
Firm Size
Total Bank Relationships
Commerzbank Relationships
Two-Digits NACE Codes Industry Description Large Medium Small Very Large Mean Median Max. Value Mean Median Max. Value Mean Share per County (%) Total Firms
70 Activities of head offices; management consultancy activities 1466 4625 59026 1287 1.5 1.0 12 0.183 0 2 11.033 66404
43 Specialised construction activities 450 16479 31996 29 1.6 1.0 8 0.094 0 2 9.782 48954
46 Wholesale trade, except of motor vehicles and motorcycles 2508 16914 28475 893 1.7 1.0 11 0.215 0 2 8.435 48790
68 Real estate activities 2998 8946 29306 451 1.4 1.0 15 0.148 0 1 6.716 41701
47 Retail trade, except of motor vehicles and motorcycles 806 11026 29296 217 1.5 1.0 10 0.108 0 4 8.237 41345
45 Wholesale and retail trade and repair of motor vehicles and motorcycles 652 6744 10989 177 1.6 1.0 10 0.104 0 1 3.993 18562
71 Architectural and engineering activities; technical testing and analysis 468 5208 10592 80 1.4 1.0 8 0.165 0 1 2.843 16348
25 Manufacture of fabricated metal products, except machinery and equipment 708 7785 6651 112 1.8 2.0 15 0.178 0 1 3.078 15256
62 Computer programming, consultancy and related activities 557 4164 9606 129 1.3 1.0 6 0.172 0 1 2.092 14456
41 Construction of buildings 580 5156 7161 81 1.5 1.0 15 0.088 0 1 2.716 12978
82 Office administrative, office support and other business support activities 645 3217 8711 163 1.4 1.0 8 0.176 0 1 2.016 12736
94 Activities of membership organisations 298 1962 10128 46 1.3 1.0 6 0.083 0 1 1.836 12434
93 Sports activities and amusement and recreation activities 134 1521 7979 20 1.3 1.0 9 0.052 0 1 1.856 9654
66 Activities auxiliary to financial services and insurance activities 220 1163 8180 87 1.4 1.0 6 0.177 0 1 1.592 9650
28 Manufacture of machinery and equipment n.e.c. 912 4621 3195 234 1.9 2.0 9 0.276 0 2 1.752 8962
49 Land transport and transport via pipelines 444 4277 3930 60 1.5 1.0 8 0.108 0 1 1.845 8711
64 Financial service activities, except insurance and pension funding 659 1320 6202 429 1.5 1.0 10 0.207 0 3 1.304 8610
96 Other personal service activities 312 2186 5773 49 1.3 1.0 6 0.125 0 2 1.431 8320
69 Legal and accounting activities 98 2597 4791 24 1.7 1.0 11 0.190 0 1 1.141 7510
52 Warehousing and support activities for transportation 542 3101 3094 133 1.6 1.0 8 0.216 0 1 1.196 6870
74 Other professional, scientific and technical activities 218 1152 5029 84 1.4 1.0 7 0.171 0 1 0.984 6483
81 Services to buildings and landscape activities 681 2717 2926 81 1.5 1.0 7 0.131 0 1 1.125 6405
88 Social work activities without accommodation 957 2660 2153 129 1.4 1.0 7 0.058 0 1 1.060 5899
32 Other manufacturing 196 1925 3301 41 1.5 1.0 6 0.156 0 2 1.062 5463
73 Advertising and market research 104 1219 3971 19 1.4 1.0 5 0.181 0 1 0.720 5313
56 Food and beverage service activities 169 2316 2795 14 1.2 1.0 5 0.101 0 2 0.874 5294
85 Education 274 1811 2834 15 1.3 1.0 9 0.108 0 1 0.834 4934
10 Manufacture of food products 597 2388 1731 159 1.6 1.0 8 0.118 0 1 1.077 4875
01 Crop and animal production, hunting and related service activities 158 2501 2203 11 1.4 1.0 6 0.066 0 1 1.520 4873
26 Manufacture of computer, electronic and optical products 429 2101 2023 116 1.7 1.0 10 0.262 0 1 0.890 4669
55 Accommodation 148 2106 2114 13 1.4 1.0 6 0.089 0 1 0.892 4381
77 Rental and leasing activities 130 1180 2865 24 1.5 1.0 6 0.147 0 1 0.803 4199
35 Electricity, gas, steam and air conditioning supply 628 1149 2061 283 1.5 1.0 7 0.157 0 1 0.973 4121
86 Human health activities 754 1580 1342 333 1.5 1.0 6 0.101 0 1 0.762 4009
18 Printing and reproduction of recorded media 111 1244 2584 15 1.8 2.0 7 0.149 0 1 0.684 3954
22 Manufacture of rubber and plastic products 442 2075 1286 73 1.9 2.0 6 0.261 0 1 0.831 3876
87 Residential care activities 803 2212 704 111 1.5 1.0 13 0.064 0 1 0.795 3830
16 Manufacture of wood and of products of wood and cork, except furniture; manufacture of articles of straw and plaiting materials 104 1482 2019 17 1.7 2.0 9 0.106 0 1 0.849 3622
23 Manufacture of other non-metallic mineral products 210 1398 1672 49 1.8 2.0 8 0.178 0 1 0.778 3329
42 Civil engineering 177 1934 1094 24 1.6 1.0 8 0.133 0 1 0.740 3229
79 Travel agency, tour operator and other reservation service and related activities 52 662 2428 12 1.4 1.0 5 0.158 0 2 0.541 3154
27 Manufacture of electrical equipment 287 1439 1173 98 1.8 2.0 6 0.254 0 1 0.584 2997
38 Waste collection, treatment and disposal activities; materials recovery 234 1270 1135 36 1.6 1.0 7 0.177 0 1 0.604 2675
58 Publishing activities 129 710 1655 30 1.7 1.0 8 0.193 0 1 0.411 2524
33 Repair and installation of machinery and equipment 64 856 1259 12 1.6 1.0 7 0.158 0 1 0.469 2191
78 Employment activities 341 1012 744 46 1.4 1.0 6 0.223 0 3 0.407 2143
59 Motion picture, video and television programme production, sound recording and music publishing activities 44 463 1528 13 1.3 1.0 5 0.209 0 1 0.285 2048
72 Scientific research and development 163 703 1085 57 1.3 1.0 6 0.188 0 1 0.396 2008
20 Manufacture of chemicals and chemical products 256 829 673 124 1.9 2.0 8 0.340 0 1 0.388 1882
31 Manufacture of furniture 82 754 834 12 1.7 2.0 7 0.136 0 1 0.378 1682
92 Gambling and betting activities 27 395 900 11 1.2 1.0 6 0.134 0 1 0.295 1333
24 Manufacture of basic metals 179 687 382 64 1.9 2.0 8 0.300 0 1 0.316 1312
13 Manufacture of textiles 94 577 605 13 1.8 2.0 6 0.239 0 1 0.334 1289
08 Other mining and quarrying 58 631 506 5 1.7 2.0 9 0.141 0 1 0.367 1200
84 Public administration and defence; compulsory social security 114 381 557 15 1.4 1.0 5 0.061 0 1 0.281 1067
29 Manufacture of motor vehicles, trailers and semi-trailers 116 473 408 66 1.8 2.0 10 0.246 0 2 0.272 1063
95 Repair of computers and personal and household goods 14 204 711 4 1.4 1.0 5 0.133 0 1 0.214 933
11 Manufacture of beverages 75 457 351 29 2.0 2.0 5 0.134 0 1 0.320 912
17 Manufacture of paper and paper products 113 458 282 48 2.0 2.0 6 0.314 0 1 0.250 901
50 Water transport 47 356 478 16 1.3 1.0 6 0.203 0 1 0.426 897
63 Information service activities 41 228 584 16 1.3 1.0 5 0.203 0 1 0.184 869
90 Creative, arts and entertainment activities 66 263 532 3 1.3 1.0 5 0.120 0 1 0.195 864
80 Security and investigation activities 136 341 291 13 1.6 1.0 6 0.207 0 1 0.201 781
14 Manufacture of wearing apparel 48 276 338 14 1.9 2.0 5 0.235 0 1 0.217 676
30 Manufacture of other transport equipment 36 256 262 26 1.7 1.0 6 0.228 0 1 0.191 580
21 Manufacture of basic pharmaceutical products and pharmaceutical preparations 81 214 182 66 1.9 2.0 8 0.330 0 1 0.170 543
61 Telecommunications 45 154 327 15 1.3 1.0 4 0.226 0 1 0.158 541
53 Postal and courier activities 74 181 243 14 1.3 1.0 4 0.176 0 1 0.162 512
37 Sewerage 27 225 241 6 1.5 1.0 5 0.132 0 1 0.172 499
91 Libraries, archives, museums and other cultural activities 30 133 259 3 1.4 1.0 6 0.120 0 1 0.158 425
60 Programming and broadcasting activities 15 132 189 6 1.4 1.0 5 0.228 0 1 0.171 342
15 Manufacture of leather and related products 20 122 183 7 1.9 2.0 7 0.226 0 1 0.197 332
02 Forestry and logging 7 113 209 2 1.3 1.0 4 0.076 0 1 0.176 331
36 Water collection, treatment and supply 86 105 82 13 1.7 1.0 6 0.122 0 1 0.149 286
39 Remediation activities and other waste management services 7 65 88 1 1.5 1.0 3 0.217 0 1 0.117 161
51 Air transport 8 35 95 3 1.4 1.0 4 0.255 0 1 0.106 141
03 Fishing and aquaculture 3 27 54 0 1.4 1.0 3 0.095 0 1 0.154 84
75 Veterinary activities 3 28 40 0 1.3 1.0 5 0.085 0 1 0.090 71
19 Manufacture of coke and refined petroleum products 11 19 18 20 2.0 2.0 6 0.456 0 1 0.104 68
09 Mining support service activities 8 22 10 4 1.4 1.0 3 0.227 0 1 0.138 44
12 Manufacture of tobacco products 4 4 4 7 1.7 1.0 4 0.368 0 1 0.081 19
06 Extraction of crude petroleum and natural gas 4 4 2 7 1.6 1.0 3 0.471 0 1 0.100 17
97 Activities of households as employers of domestic personnel 0 6 7 0 1.5 1.0 3 0.000 0 0 0.061 13
07 Mining of metal ores 0 5 1 2 1.6 1.5 3 0.250 0 1 0.050 8
99 Activities of extraterritorial organisations and bodies 0 1 5 0 1.7 1.5 3 0.167 0 1 0.105 6
05 Mining of coal and lignite 0 2 0 2 1.8 1.5 3 0.250 0 1 0.129 4
98 Undifferentiated goods- and services-producing activities of private households for own use 0 0 1 0 1.0 1.0 1 0.000 0 0 0.040 1
65 Insurance, reinsurance and pension funding, except compulsory social security 0 0 0 0 0.0 0.0 0 0.000 0 0 0.000 0

A large part of the firms are in management consultancy activities, followed by construction and wholesale trade. On average, heavy industry firms have the most number of bank relationships, whereas the most number of bank relationships with Commerzbank are on wholesale trade, manifacture of machinery and equipment, warehousing, manufacture of electronic products and manufacture of rubber and plastic products. We also report the average industry share at county level. We consider industry shares at county level as frequency weight for the size of an industry within a county.

Exploring the Financial Panel and Firm Size Categories

In this section, we explore the financial variables we fetched from the financials_x tables in Amadeus. In primis, we remove duplicates for the entire financials data where there are both consolidated and unconsolidated data. We summarize the financial variables for the entire sample after filtering duplicates by firm size category, to make some sense of what it means to be a firm of size X in the Amadeus data, how complete are the financial information and which years are more prominent, given the existing survivorship bias.

Table 6: Summary Statistics of the universe of German firms' financials in extracted from Amadeus database.
Variable Firm Size Overall, N = 8138930
Large, N = 342937 Medium, N = 2275628 Small, N = 5429817 Very Large, N = 90548
Closing Date (Year)
N missing (% missing) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0)
Mean 2015.213 2015.279 2016.426 2015.175 2016.04
SD 3.173 3.275 3.431 3.181 3.418
Minimum 1999 1997 1952 1999 1952
Median (IQR) 2016 (2013 - 2018) 2016 (2013 - 2018) 2017 (2014 - 2019) 2015 (2013 - 2018) 2017 (2014 - 2019)
Maximum 2022 2022 2022 2022 2022
Number of Employees
N missing (% missing) 103687 (0.302) 1059752 (0.466) 1391033 (0.256) 11718 (0.129) 2566190 (0.315)
Mean 160.869 31.06 4.158 2204.672 47.884
SD 210.244 138.557 195.517 14106.239 1707.729
Minimum 1 0 0 1 0
Median (IQR) 114 (28 - 219) 21 (12 - 39) 2 (1 - 5) 522 (148 - 1327) 3 (1 - 11)
Maximum 30500 90335 161000 655722 655722
Cost of Employees (EUR Mln.)
N missing (% missing) 184970 (0.539) 2076838 (0.913) 5368239 (0.989) 17922 (0.198) 7647969 (0.94)
Mean 8.688 2.308 0.596 111.285 20.267
SD 61.308 2.195 14.579 826.275 321.991
Minimum -20.427 -8.806 -1.239 -64.913 -64.913
Median (IQR) 6.361 (3.185 - 11.05) 1.75 (0.68 - 3.387) 0.204 (0.079 - 0.382) 27.925 (10.015 - 62.945) 3.033 (0.753 - 8.002)
Maximum 22660 75.643 1338 42914 42914
Operating Revenues (EUR Mln.)
N missing (% missing) 193479 (0.564) 1700911 (0.747) 4413193 (0.813) 14291 (0.158) 6321874 (0.777)
Mean 33.137 4.155 0.999 633.637 31.191
SD 173.836 22.383 51.42 4293.931 890.996
Minimum -51.596 -21.111 -4 -173.11 -173.11
Median (IQR) 21.475 (10.7 - 46.026) 2.2 (1.259 - 4.262) 0.365 (0.15 - 0.73) 154.136 (88.344 - 328.826) 0.9 (0.3 - 3.027)
Maximum 52570 6242.75 40000 264085 264085
Working Capital (EUR Mln.)
N missing (% missing) 107518 (0.314) 1104205 (0.485) 4479381 (0.825) 11005 (0.122) 5702109 (0.701)
Mean 3.67 0.567 0.123 71.911 3.022
SD 44.127 4.743 1.538 599.013 109.879
Minimum -278 -45.704 -423 -3038.101 -3038.101
Median (IQR) 0.909 (0 - 4.21) 0.095 (0 - 0.531) 0.007 (0 - 0.09) 14.698 (1.533 - 41.965) 0.044 (0 - 0.402)
Maximum 20306.179 3000.496 673 41938 41938
Current Assets (EUR Mln.)
N missing (% missing) 19543 (0.057) 203751 (0.09) 3508347 (0.646) 2990 (0.033) 3734631 (0.459)
Mean 10.575 1.702 0.421 279.997 7.327
SD 73.703 20.436 5.948 3508.954 496.892
Minimum 0 -6.147 -30.555 -0.628 -30.555
Median (IQR) 4.863 (1.738 - 12.47) 0.839 (0.362 - 1.94) 0.199 (0.05 - 0.48) 48.59 (20.319 - 125.123) 0.5 (0.159 - 1.433)
Maximum 25472.361 13221.685 2818.342 302073 302073
Total Assets (EUR Mln.)
N missing (% missing) 23454 (0.068) 231118 (0.102) 3525485 (0.649) 4098 (0.045) 3784155 (0.465)
Mean 26.875 3.124 0.809 660.647 16.907
SD 117.127 25.891 85.557 6285.792 892.926
Minimum -24.489 -9.88 -2.215 -3.055 -24.489
Median (IQR) 16.01 (4.598 - 32.852) 1.552 (0.647 - 3.635) 0.325 (0.086 - 0.697) 119.833 (45.466 - 314.089) 0.816 (0.291 - 2.622)
Maximum 38878.093 13221.685 110982.606 497114 497114
Current Liabilities (EUR Mln.)
N missing (% missing) 19363 (0.056) 211989 (0.093) 3511461 (0.647) 2796 (0.031) 3745609 (0.46)
Mean 5.467 0.733 0.214 165.764 4.151
SD 112.898 30.03 37.61 1865.356 268.354
Minimum -61019.547 -4.744 -0.728 0 -61019.547
Median (IQR) 1.714 (0.099 - 5.824) 0.112 (0 - 0.576) 0.006 (0 - 0.109) 23.723 (8.088 - 65.46) 0.048 (0 - 0.409)
Maximum 14871.1 40115.384 51087.048 141614 141614
Current Loans (EUR Mln.)
N missing (% missing) 99305 (0.29) 1064366 (0.468) 4380980 (0.807) 9578 (0.106) 5554229 (0.682)
Mean 1.063 0.113 0.025 28.947 1.07
SD 5.754 11.59 2.645 638.534 113.431
Minimum -0.305 -4.288 -0.189 0 -4.288
Median (IQR) 0 (0 - 0.5) 0 (0 - 0) 0 (0 - 0) 0.439 (0 - 7.327) 0 (0 - 0)
Maximum 1005.176 12700 2371.493 56184 56184
Long-Term Debt (EUR Mln.)
N missing (% missing) 20183 (0.059) 204328 (0.09) 3546863 (0.653) 3187 (0.035) 3774561 (0.464)
Mean 7.773 1.064 0.342 148.769 4.205
SD 120.28 31.592 92.62 1766.655 261.052
Minimum -2217.233 -40075.269 -51087.024 -24759 -51087.024
Median (IQR) 0.798 (0 - 6.238) 0.212 (0.005 - 0.911) 0.026 (0 - 0.177) 4.724 (0 - 37.64) 0.094 (0 - 0.561)
Maximum 61100 12861.075 111719.787 142682 142682
Total Shareholders Funds (EUR Mln.)
N missing (% missing) 23454 (0.068) 231118 (0.102) 3525482 (0.649) 4098 (0.045) 3784152 (0.465)
Mean 26.873 3.124 0.809 660.616 16.906
SD 117.124 25.891 85.557 6285.783 892.924
Minimum -24.489 -9.88 -39.132 -3.055 -39.132
Median (IQR) 16.009 (4.598 - 32.85) 1.552 (0.647 - 3.635) 0.324 (0.086 - 0.697) 119.833 (45.466 - 314.089) 0.816 (0.291 - 2.622)
Maximum 38878.093 13221.685 110982.606 497114 497114
Total Shareholders Funds and Liabilities (EUR Mln.)
N missing (% missing) 23232 (0.068) 229200 (0.101) 3520623 (0.648) 4064 (0.045) 3777119 (0.464)
Mean 11.643 1.101 0.2 244.644 6.308
SD 85.186 13.8 7.065 1809.455 258.298
Minimum -5617.419 -879 -888.562 -7460.1 -7460.1
Median (IQR) 3.798 (0.629 - 13.761) 0.366 (0.089 - 1.166) 0.063 (0.016 - 0.231) 38.5 (9.85 - 122.419) 0.196 (0.033 - 0.781)
Maximum 34542.561 10251.128 2602.375 128783 128783
Liquidity Ratio (%)
N missing (% missing) 94987 (0.277) 1012177 (0.445) 4457241 (0.821) 11292 (0.125) 5575697 (0.685)
Mean 4.374 4.55 5.946 2.886 5.011
SD 10.513 10.349 12.388 6.991 11.128
Minimum 0 0 0 0 0
Median (IQR) 1.42 (0.71 - 3.19) 1.46 (0.68 - 3.62) 1.72 (0.73 - 4.92) 1.32 (0.77 - 2.41) 1.53 (0.7 - 3.95)
Maximum 100 100 100 99.91 100
Solvency Ratio (%)
N missing (% missing) 25940 (0.076) 260862 (0.115) 3620250 (0.667) 4426 (0.049) 3911478 (0.481)
Mean 39.034 34.859 41.648 39.777 38.178
SD 31.351 31.963 39.368 27.845 35.36
Minimum -99.94 -100 -100 -99.98 -100
Median (IQR) 36.56 (14.96 - 61.83) 32.4 (11.26 - 58.57) 42.15 (12.82 - 74.83) 37.62 (20 - 58.03) 36.41 (12.28 - 65.42)
Maximum 100 100 100 100 100
Credit Period (days)
N missing (% missing) 216933 (0.633) 1946284 (0.855) 5284454 (0.973) 17536 (0.194) 7465207 (0.917)
Mean 17.275 8.346 9.991 21.026 11.745
SD 34.653 30.196 40.62 32.784 34.12
Minimum 0 0 0 0 0
Median (IQR) 11 (3 - 21) 0 (0 - 6) 0 (0 - 3) 16 (8 - 26) 0 (0 - 14)
Maximum 988 997 993 995 997

From the summary statistics we do see the survivorship bias for all firms in the sample. The closing date year is 2014 at the 25th percentile, which does not give us much hope to have information at the years before the credit shock. The most complete information comes from the number of employees, current assets, current liabilities, total assets, long-term debt, total equity and right-hand side of the balance sheet and solvency ratio. We try to isolate those variables for the entire period available for those firms restricted with the incorporation date and the bank relationships data.

Table 7: Summary Statistics of the subsample of German firms established before 2007 of which we have bank relationships for the entire period.
Variable Firm Size Overall, N = 4321049
Large, N = 247110 Medium, N = 1552278 Small, N = 2450526 Very Large, N = 71135
Closing Date (Year)
N missing (% missing) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0)
Mean 2014.98 2014.943 2015.548 2015.01 2015.289
SD 3.186 3.286 3.613 3.191 3.481
Minimum 2001 1997 1980 1999 1980
Median (IQR) 2015 (2012 - 2018) 2015 (2012 - 2018) 2016 (2013 - 2018) 2015 (2012 - 2018) 2016 (2013 - 2018)
Maximum 2022 2022 2022 2022 2022
Number of Employees
N missing (% missing) 65055 (0.263) 754612 (0.486) 801733 (0.327) 7733 (0.109) 1629133 (0.377)
Mean 173.487 34.361 5.693 2320.569 80.057
SD 217.487 110.847 286.623 15015.627 2343.143
Minimum 1 1 1 1 1
Median (IQR) 130 (43 - 230) 23 (15 - 43) 3 (1 - 6) 541 (170 - 1334.75) 6 (2 - 21)
Maximum 30500 90335 161000 655722 655722
Cost of Employees (EUR Mln.)
N missing (% missing) 114824 (0.465) 1388467 (0.894) 2410268 (0.984) 11612 (0.163) 3925171 (0.908)
Mean 8.655 2.433 0.708 117.291 21.607
SD 65.319 2.185 17.924 881.833 346.424
Minimum -9.468 -4.613 -1.125 -64.913 -64.913
Median (IQR) 6.363 (3.278 - 10.934) 1.929 (0.799 - 3.526) 0.239 (0.113 - 0.411) 28.142 (10.432 - 62.972) 3.325 (0.989 - 8.274)
Maximum 22660 75.643 1338 42914 42914
Current Assets (EUR Mln.)
N missing (% missing) 15932 (0.064) 123548 (0.08) 1393407 (0.569) 2381 (0.033) 1535268 (0.355)
Mean 11.253 1.821 0.456 311.754 9.735
SD 65.248 13.755 4.74 3938.531 620.976
Minimum 0 -6.147 -0.272 0 -6.147
Median (IQR) 5.746 (2.234 - 13.627) 0.97 (0.432 - 2.151) 0.262 (0.089 - 0.541) 50.748 (22.378 - 129.451) 0.641 (0.242 - 1.823)
Maximum 24066.329 10051.702 2204 302073 302073
Total Assets (EUR Mln.)
N missing (% missing) 18873 (0.076) 143466 (0.092) 1404087 (0.573) 3272 (0.046) 1569698 (0.363)
Mean 26.153 3.071 0.741 705.57 21.427
SD 93.015 21.027 21.694 7024.392 1109.066
Minimum -24.489 -8.159 -1.559 -3.055 -24.489
Median (IQR) 15.581 (5.006 - 31.818) 1.654 (0.729 - 3.668) 0.391 (0.145 - 0.755) 118.756 (47.45 - 306.222) 1.007 (0.396 - 3.116)
Maximum 29795.11 11845.434 7638 497114 497114
Current Liabilities (EUR Mln.)
N missing (% missing) 15829 (0.064) 130426 (0.084) 1396433 (0.57) 2251 (0.032) 1544939 (0.358)
Mean 5.802 0.744 0.187 183.6 5.491
SD 38.794 34.4 9.135 2085.905 330.958
Minimum -6.379 -4.744 -0.728 0 -6.379
Median (IQR) 2.106 (0.264 - 6.301) 0.121 (0 - 0.616) 0.012 (0 - 0.126) 24.823 (9.465 - 66.901) 0.071 (0 - 0.516)
Maximum 14871.1 40115.384 7708.979 141614 141614
loan
N missing (% missing) 66620 (0.27) 720290 (0.464) 1873532 (0.765) 6547 (0.092) 2666989 (0.617)
Mean 1.114 0.11 0.016 32.303 1.444
SD 5.693 0.911 0.543 711.477 140.743
Minimum -0.118 -4.288 -0.004 0 -4.288
Median (IQR) 0 (0 - 0.668) 0 (0 - 0) 0 (0 - 0) 0.663 (0 - 7.73) 0 (0 - 0)
Maximum 1005.176 373.27 254.172 56184 56184
Long-Term Debt (EUR Mln.)
N missing (% missing) 15731 (0.064) 121879 (0.079) 1404137 (0.573) 2378 (0.033) 1544125 (0.357)
Mean 6.414 0.898 0.257 152.907 4.88
SD 30.554 35.369 10.907 1965.548 311.42
Minimum -2217.233 -40075.269 -7701.03 -24759 -40075.269
Median (IQR) 0.784 (0 - 5.019) 0.222 (0.01 - 0.862) 0.039 (0 - 0.196) 4.894 (0 - 34.915) 0.121 (0 - 0.619)
Maximum 11781.267 8426.402 3721.862 142682 142682
Total Shareholders Funds (EUR Mln.)
N missing (% missing) 18873 (0.076) 143466 (0.092) 1404085 (0.573) 3272 (0.046) 1569696 (0.363)
Mean 26.153 3.071 0.741 705.53 21.426
SD 93.015 21.027 21.694 7024.382 1109.063
Minimum -24.489 -8.159 -39.132 -3.055 -39.132
Median (IQR) 15.58 (5.006 - 31.818) 1.654 (0.729 - 3.668) 0.391 (0.145 - 0.755) 118.756 (47.45 - 306.222) 1.007 (0.396 - 3.116)
Maximum 29795.11 11845.434 7638 497114 497114
Total Shareholders Funds and Liabilities (EUR Mln.)
N missing (% missing) 18708 (0.076) 142270 (0.092) 1401960 (0.572) 3252 (0.046) 1566190 (0.362)
Mean 11.676 1.167 0.222 256.047 7.959
SD 67.082 11.701 7.909 1989.868 315.592
Minimum -978.925 -879 -227.534 -7460.1 -7460.1
Median (IQR) 4.498 (0.992 - 14.161) 0.448 (0.126 - 1.313) 0.093 (0.026 - 0.277) 40.773 (11.571 - 120.581) 0.28 (0.059 - 1.038)
Maximum 29065.231 9184.535 1838 128783 128783
Solvency Ratio (%)
N missing (% missing) 20240 (0.082) 159931 (0.103) 1450104 (0.592) 3492 (0.049) 1633767 (0.378)
Mean 40.776 36.82 41.144 40.182 38.848
SD 29.203 30.863 38.03 26.237 33.552
Minimum -99.94 -100 -100 -98.88 -100
Median (IQR) 38.99 (18.81 - 62.29) 35.25 (13.9 - 60.18) 41.73 (14.19 - 71.93) 38.38 (21.67 - 57.415) 37.79 (14.62 - 64.36)
Maximum 100 100 100 100 100

We compare the current firms financial information with the firms we were using in the previous sample in the draft, to see whether we can retrieve some financial data for those. The numbers are actually very similar.

Table 8: Summary Statistics of the subsample of German firms established before 2007 of which we have bank relationships for the entire period, as calculated in the previous draft.
Variable Firm Size Overall, N = 4346076
Large, N = 249766 Medium, N = 1563759 Small, N = 2460638 Very Large, N = 71913
Closing Date (Year)
N missing (% missing) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0)
Mean 2014.969 2014.927 2015.486 2015 2015.247
SD 3.185 3.284 3.606 3.191 3.474
Minimum 2001 1997 1980 1999 1980
Median (IQR) 2015 (2012 - 2018) 2015 (2012 - 2018) 2016 (2013 - 2018) 2015 (2012 - 2018) 2016 (2013 - 2018)
Maximum 2022 2022 2022 2022 2022
Number of Employees
N missing (% missing) 65985 (0.264) 762028 (0.487) 824413 (0.335) 7842 (0.109) 1660268 (0.382)
Mean 173.082 34.376 5.761 2299.473 80.469
SD 216.98 110.636 287.702 14880.696 2337.034
Minimum 1 1 1 1 1
Median (IQR) 130 (43 - 229) 23 (15 - 43) 3 (1 - 6) 539 (169 - 1330) 7 (2 - 21)
Maximum 30500 90335 161000 655722 655722
Cost of Employees (EUR Mln.)
N missing (% missing) 115970 (0.464) 1397883 (0.894) 2418654 (0.983) 11711 (0.163) 3944218 (0.908)
Mean 8.653 2.436 0.718 116.169 21.364
SD 64.954 2.197 17.556 874.512 342.928
Minimum -9.468 -8.806 -1.125 -64.913 -64.913
Median (IQR) 6.36 (3.281 - 10.944) 1.928 (0.798 - 3.53) 0.241 (0.113 - 0.418) 28.005 (10.393 - 62.79) 3.311 (0.977 - 8.241)
Maximum 22660 75.643 1338 42914 42914
Current Assets (EUR Mln.)
N missing (% missing) 15663 (0.063) 120345 (0.077) 1362854 (0.554) 2356 (0.033) 1501218 (0.345)
Mean 11.375 1.823 0.464 309.156 9.599
SD 83.537 13.694 5.663 3915.113 614.579
Minimum 0 -6.147 -0.376 0 -6.147
Median (IQR) 5.747 (2.229 - 13.638) 0.97 (0.431 - 2.152) 0.26 (0.086 - 0.54) 50.646 (22.255 - 129.105) 0.634 (0.237 - 1.806)
Maximum 25472.361 10051.702 2818.342 302073 302073
Total Assets (EUR Mln.)
N missing (% missing) 18651 (0.075) 140476 (0.09) 1373876 (0.558) 3253 (0.045) 1536256 (0.353)
Mean 26.291 3.079 0.879 698.941 21.141
SD 106.595 20.874 108.758 6981.131 1099.168
Minimum -24.489 -8.159 -1.559 -3.055 -24.489
Median (IQR) 15.606 (5.01 - 31.868) 1.656 (0.729 - 3.675) 0.388 (0.141 - 0.755) 118.28 (47.073 - 305.286) 0.995 (0.39 - 3.09)
Maximum 29795.11 11845.434 110982.606 497114 497114
Current Liabilities (EUR Mln.)
N missing (% missing) 15564 (0.062) 127260 (0.081) 1365883 (0.555) 2222 (0.031) 1510929 (0.348)
Mean 5.822 0.748 0.196 182.246 5.415
SD 38.588 34.228 9.275 2073.22 327.405
Minimum -6.379 -4.744 -0.728 0 -6.379
Median (IQR) 2.104 (0.263 - 6.324) 0.121 (0 - 0.617) 0.011 (0 - 0.125) 24.794 (9.405 - 66.558) 0.069 (0 - 0.51)
Maximum 14871.1 40115.384 7708.979 141614 141614
loan
N missing (% missing) 67046 (0.268) 722962 (0.462) 1862165 (0.757) 6597 (0.092) 2658770 (0.612)
Mean 1.109 0.111 0.024 32.104 1.427
SD 5.682 0.94 3.153 707.517 139.366
Minimum -0.118 -4.288 -0.004 0 -4.288
Median (IQR) 0 (0 - 0.658) 0 (0 - 0) 0 (0 - 0) 0.642 (0 - 7.686) 0 (0 - 0)
Maximum 1005.176 373.27 2371.493 56184 56184
Long-Term Debt (EUR Mln.)
N missing (% missing) 15495 (0.062) 118613 (0.076) 1374587 (0.559) 2358 (0.033) 1511053 (0.348)
Mean 6.49 0.901 0.388 151.015 4.849
SD 54.426 35.109 107.882 1952.919 315.343
Minimum -2217.233 -40075.269 -7701.03 -24759 -40075.269
Median (IQR) 0.772 (0 - 5) 0.222 (0.01 - 0.864) 0.039 (0 - 0.197) 4.773 (0 - 34.5) 0.119 (0 - 0.614)
Maximum 21859.46 8426.402 111719.787 142682 142682
Total Shareholders Funds (EUR Mln.)
N missing (% missing) 18651 (0.075) 140476 (0.09) 1373874 (0.558) 3253 (0.045) 1536254 (0.353)
Mean 26.29 3.079 0.879 698.901 21.14
SD 106.595 20.874 108.758 6981.121 1099.165
Minimum -24.489 -8.159 -39.132 -3.055 -39.132
Median (IQR) 15.604 (5.009 - 31.867) 1.655 (0.729 - 3.675) 0.388 (0.141 - 0.755) 118.28 (47.073 - 305.286) 0.995 (0.39 - 3.09)
Maximum 29795.11 11845.434 110982.606 497114 497114
Total Shareholders Funds and Liabilities (EUR Mln.)
N missing (% missing) 18485 (0.074) 139242 (0.089) 1371620 (0.557) 3229 (0.045) 1532576 (0.353)
Mean 11.714 1.168 0.22 254.255 7.847
SD 66.84 11.63 7.839 1978.637 312.348
Minimum -978.925 -706.907 -888.562 -7460.1 -7460.1
Median (IQR) 4.492 (0.99 - 14.175) 0.447 (0.125 - 1.313) 0.091 (0.025 - 0.274) 40.382 (11.414 - 120.207) 0.275 (0.057 - 1.026)
Maximum 29065.231 9184.535 1838 128783 128783
Solvency Ratio (%)
N missing (% missing) 20038 (0.08) 157372 (0.101) 1423645 (0.579) 3480 (0.048) 1604535 (0.369)
Mean 40.775 36.769 41.037 40.132 38.803
SD 29.254 30.911 38.221 26.282 33.705
Minimum -99.94 -100 -100 -98.88 -100
Median (IQR) 38.97 (18.72 - 62.323) 35.19 (13.83 - 60.17) 41.63 (13.95 - 72.06) 38.32 (21.58 - 57.38) 37.75 (14.49 - 64.45)
Maximum 100 100 100 100 100

For those firms and those variables, we would like to understand how many we have for the years before the credit shock, from 2000 to 2008 as reference years. From the table below, we see that information of number and cost of employees is very limited, whereas information on current assets, current liabilities and long-term debt is more available. For weighting using financials data, it makes sense to use alternatively the three years before the shock, which are 2006, 2007, and 2008, with more emphasis on 2007 as we have more information than 2006 and it is safe enough to not be affected by the credit shock than 2008, net of the delay in reporting of Amadeus.

Table 9: Frequency of non-missing values for the financial variables of most interest of the firms established before 2007 and of which we have the bank relationships, between 2000 and 2008.
Closing Date (Year) Non-Missing Values
Number of Employees Cost of Employees Current Assets Total Assets Current Liabilities Long-Term Debt Total Equity Total Equity and Liabilities Solvency Ratio
2000 8 11 14 14 14 13 14 14 14
2001 14 30 38 38 38 38 38 38 38
2002 23 60 90 90 90 89 90 90 86
2003 24 157 256 256 256 254 256 257 249
2004 42 327 719 719 719 714 719 719 688
2005 199 627 8615 8616 8615 8435 8616 8621 8130
2006 1439 1003 20630 20630 20631 20117 20630 20649 19487
2007 2663 1782 30971 30973 30985 30405 30973 31005 29362
2008 4881 4687 46717 46717 46733 45896 46717 46754 44421
Table 10: Summary Statistics of the subsample of German firms established before 2007 of which we have bank relationships for the years 2006, 2007 and 2008 by firm size category.
Variable 2006 2007 2008
Large, N = 476 Medium, N = 4599 Small, N = 16806 Very Large, N = 139 Overall, N = 22020 Large, N = 902 Medium, N = 7766 Small, N = 23857 Very Large, N = 277 Overall, N = 32802 Large, N = 1540 Medium, N = 14403 Small, N = 32556 Very Large, N = 434 Overall, N = 48933
Number of Employees
N missing (% missing) 419 (0.88) 4312 (0.938) 15755 (0.937) 95 (0.683) 20581 (0.935) 770 (0.854) 7104 (0.915) 22064 (0.925) 201 (0.726) 30139 (0.919) 1251 (0.812) 12823 (0.89) 29692 (0.912) 286 (0.659) 44052 (0.9)
Mean 125.632 21.08 9.925 1883.364 74.017 197.992 27.715 10.713 1544.579 67.998 156.685 34.282 9.424 1151.176 60.809
SD 248.558 37.935 58.999 4552.991 853.782 432.489 50.27 54.594 3881.245 708.489 350.204 117.475 45.581 3095.102 582.982
Minimum 1 1 1 2 1 1 1 1 2 1 1 1 1 1 1
Median (IQR) 46 (5 - 159) 7 (3 - 23) 4 (2 - 7) 697 (122.25 - 2177.25) 5 (2 - 10) 123 (18.5 - 244.25) 11 (4 - 33) 4 (2 - 7) 544 (118 - 1470.5) 5 (2 - 13.5) 76 (16 - 185) 18 (6 - 41) 4 (2 - 8) 420.5 (39 - 938.5) 6 (2 - 20)
Maximum 1753 317 1754 29774 29774 4419 822 1635 30791 30791 4888 4354 1507 31971 31971
Cost of Employees (EUR Mln.)
N missing (% missing) 411 (0.863) 4341 (0.944) 16177 (0.963) 88 (0.633) 21017 (0.954) 753 (0.835) 7037 (0.906) 23056 (0.966) 174 (0.628) 31020 (0.946) 1180 (0.766) 11888 (0.825) 30925 (0.95) 253 (0.583) 44246 (0.904)
Mean 7.37 1.325 0.45 83.268 5.335 7.609 1.292 0.558 63.638 5.094 5.826 1.146 0.445 47.97 3.07
SD 13.495 3.804 4.65 255.816 60.191 15.019 2.521 4.401 197.253 49.722 11.694 1.335 3.033 160.202 32.914
Minimum 0.009 0.002 0 0.001 0 0.006 0 0 0.016 0 0.002 0 0 0.011 0
Median (IQR) 4.381 (0.727 - 8.926) 0.479 (0.13 - 1.46) 0.138 (0.055 - 0.249) 20.231 (2.968 - 62.436) 0.195 (0.073 - 0.598) 4.551 (0.792 - 8.783) 0.816 (0.329 - 1.556) 0.159 (0.062 - 0.291) 13.396 (2.247 - 51.666) 0.357 (0.128 - 1.3) 2.334 (0.7 - 6.456) 0.8 (0.395 - 1.494) 0.184 (0.091 - 0.328) 13.09 (1.806 - 38.033) 0.506 (0.186 - 1.317)
Maximum 102.381 55.485 115.551 1748.1 1748.1 132.706 51.402 111.263 1843.8 1843.8 135.239 23.173 106.58 1940.8 1940.8
Current Assets (EUR Mln.)
N missing (% missing) 2 (0.004) 29 (0.006) 1359 (0.081) 0 (0) 1390 (0.063) 5 (0.006) 38 (0.005) 1788 (0.075) 0 (0) 1831 (0.056) 12 (0.008) 54 (0.004) 2150 (0.066) 0 (0) 2216 (0.045)
Mean 10.115 0.756 0.268 198.09 1.935 34.177 0.979 0.304 299.321 4.128 7.923 1.101 0.3 176.236 2.43
SD 83.073 3.108 3.177 1205.725 100.778 803.685 4.353 3.902 3387.008 348.936 50.111 4.84 2.219 1695.91 164.425
Minimum 0 0 -0.017 0 -0.017 0 -0.061 -0.008 0 -0.061 0 -0.057 -0.001 0 -0.057
Median (IQR) 0.568 (0.054 - 4.343) 0.144 (0.05 - 0.437) 0.102 (0.037 - 0.216) 11.599 (0.317 - 57.267) 0.111 (0.04 - 0.253) 1.163 (0.124 - 5.965) 0.204 (0.071 - 0.794) 0.111 (0.041 - 0.239) 10.083 (0.539 - 62.023) 0.131 (0.047 - 0.322) 1.599 (0.234 - 6.092) 0.343 (0.109 - 1.13) 0.122 (0.045 - 0.265) 12.506 (0.867 - 57.089) 0.164 (0.059 - 0.449)
Maximum 1728.986 129 271.747 13689.7 13689.7 24066.329 244.609 478.747 56187.928 56187.928 1765.595 496.277 219.957 34711.51 34711.51
Current Liabilities (EUR Mln.)
N missing (% missing) 2 (0.004) 29 (0.006) 1358 (0.081) 0 (0) 1389 (0.063) 6 (0.007) 37 (0.005) 1774 (0.074) 0 (0) 1817 (0.055) 12 (0.008) 48 (0.003) 2140 (0.066) 0 (0) 2200 (0.045)
Mean 2.275 0.398 0.111 78.861 0.755 3.399 5.643 0.14 151.166 2.957 3.11 0.477 0.13 106.121 1.318
SD 11.437 2.857 1.097 566.573 46.845 17.865 456.305 2.406 1731.014 280.787 14.801 2.361 1.307 1212.698 117.216
Minimum 0 0 -0.053 0 -0.053 0 -0.035 -0.032 0 -0.035 0 -0.02 -0.013 0 -0.02
Median (IQR) 0.003 (0 - 0.728) 0.013 (0 - 0.138) 0.005 (0 - 0.069) 0.336 (0 - 19.348) 0.006 (0 - 0.08) 0.001 (0 - 1.097) 0.011 (0 - 0.166) 0.003 (0 - 0.067) 0.052 (0 - 17.08) 0.004 (0 - 0.085) 0.016 (0 - 1.475) 0.03 (0 - 0.285) 0.005 (0 - 0.074) 0.58 (0 - 19.477) 0.01 (0 - 0.113)
Maximum 203.353 148.916 72.869 6509.935 6509.935 329.933 40115.384 255.965 28708.723 40115.384 328.14 187.043 130.056 25069.117 25069.117
Total Assets (EUR Mln.)
N missing (% missing) 2 (0.004) 29 (0.006) 1359 (0.081) 0 (0) 1390 (0.063) 5 (0.006) 38 (0.005) 1786 (0.075) 0 (0) 1829 (0.056) 12 (0.008) 54 (0.004) 2150 (0.066) 0 (0) 2216 (0.045)
Mean 23.749 2.352 0.567 412.782 4.273 57.073 2.252 0.627 507.086 7.196 22.953 2.323 0.596 405.101 5.616
SD 90.928 30.684 15.266 1713.838 146.171 995.264 7.822 12.399 3554.525 379.098 63.32 10.411 9.995 2000.966 197.11
Minimum 0 -0.128 -0.017 0.012 -0.128 0 -0.082 -0.001 0.012 -0.082 0 -0.078 -0.005 0.01 -0.078
Median (IQR) 5.123 (0.353 - 21.858) 0.289 (0.097 - 1.723) 0.154 (0.059 - 0.32) 46.927 (4.584 - 165.029) 0.176 (0.068 - 0.426) 7.235 (0.733 - 24.351) 0.447 (0.135 - 2.274) 0.168 (0.065 - 0.357) 54.938 (7.431 - 168.966) 0.207 (0.079 - 0.581) 6.376 (1.039 - 23.89) 0.75 (0.198 - 2.528) 0.187 (0.073 - 0.396) 57.411 (8.812 - 199.495) 0.26 (0.1 - 0.845)
Maximum 1728.998 2040 1787.747 13689.7 13689.7 29795.11 337.545 1577.576 56187.928 56187.928 1765.602 991.709 1537.036 34711.51 34711.51
Long-Term Debt (EUR Mln.)
N missing (% missing) 19 (0.04) 124 (0.027) 1755 (0.104) 5 (0.036) 1903 (0.086) 21 (0.023) 125 (0.016) 2247 (0.094) 4 (0.014) 2397 (0.073) 37 (0.024) 189 (0.013) 2804 (0.086) 7 (0.016) 3037 (0.062)
Mean 4.548 1.321 0.248 89.813 1.181 6.606 -4.393 0.247 145.056 0.565 6.299 0.944 0.273 79.298 1.413
SD 16.952 43.604 4.513 626.56 55.619 31.386 458.479 3.801 1677.607 279.699 19.458 5.013 3.731 548.111 53.627
Minimum 0 -0.001 -61.208 0 -61.208 0 -40075.269 -143.231 0 -40075.269 0 -21.88 -46.664 0 -46.664
Median (IQR) 0.106 (0 - 2.231) 0.042 (0 - 0.317) 0.02 (0 - 0.107) 0.263 (0 - 11.745) 0.024 (0 - 0.138) 0.226 (0 - 3.1) 0.083 (0 - 0.542) 0.026 (0 - 0.123) 1.024 (0 - 15.083) 0.035 (0 - 0.182) 0.449 (0 - 3.973) 0.129 (0.003 - 0.717) 0.029 (0 - 0.136) 1.712 (0 - 20.466) 0.048 (0 - 0.25)
Maximum 270.46 2913 400.975 7179.716 7179.716 714.779 91.824 399.425 27479.154 27479.154 328.626 404.128 398.735 9642.27 9642.27

Weights using the Financial Information

From the financial data we have, we take into account two dimensions of the firm within the industry clusters at county level:

  • the sensitivity of a firm to liquidity constraints generated by a credit shock, which give us how the firm compared to the county-level peers would be more exposed to a short-term credit shock, and

  • the county-level firm relevance, which gives us how important is the firm for the county’s economy compared to the county-level peers.

For the first measure, we consider the current ratio, which is current assets over current liabilities. It is a type of liquidity ratio that measures a company’s ability to pay off its current liabilities with its total current assets such as cash, accounts receivable and inventories. In case of a credit shock, the firms needs to rely on liquidity to pay off current liabilities, such as the salaries, and the higher the current ratio, the better is the company’s liquidity position.

For the second measure, we can either consider the number of employees or the cost of employees, which is also indicative of the size of the firm in a non-categorical fashion. From the summary table of financials for the subsample, we see that current assets and liabilities can be negative or zero, as well as cost of employees.

It is reasonable to think that the sensitivity of a firm depends on its size and industry. Firms within an industry might respond differently to credit shocks than industries in a different industry. County-level industry composition share of firm size should be absorbed by fixed effects in the identification, but we can still compare firms within the county by their current ratio.

We take the inverse rank of firms within a county by their current ratio and divide this rank by the sum of total values of ranks at county level, such that the weights sum up to one. Ranking takes care of the negative current assets and liabilities, as well as infinite values due to zero liabilities. Identical current ratios are weighted equally. The smaller the current ratio of a firm, the higher the rank and the bigger the weight assigned at county level.

We use the number or the cost of employees at firm level to weight for the size of a firm within a county. We combine the current ratio weights and the employees weights computing two different weighted averages for each weight and then divide by 2. The following table gives a summary of the weights with the financial information at firm level.

Table 11: Summary Statistics of the within-county weights obtained using firm-level financial variables for the subset of firms and the balance sheets from 2006, 2007, and 2008.
Within-County Weights Missing Values Complete Rate (%) Mean Std. Dev. Min. Value 25th Median 75th Max. Value Histogram
rank(Current Ratio) 2006 1511 0.9314 0.0195 0.0243 0.0004 0.0044 0.0118 0.0255 0.5000 ▇▁▁▁▁
2007 1968 0.9400 0.0129 0.0162 0.0003 0.0030 0.0079 0.0169 0.5000 ▇▁▁▁▁
2008 2372 0.9515 0.0086 0.0106 0.0002 0.0020 0.0052 0.0111 0.3333 ▇▁▁▁▁
No. Employees 2006 20581 0.0653 0.2210 0.3080 0.0001 0.0120 0.0667 0.2929 1.0000 ▇▁▁▁▁
2007 30139 0.0812 0.1382 0.2453 0.0000 0.0040 0.0250 0.1300 1.0000 ▇▁▁▁▁
2008 44052 0.0997 0.0795 0.1715 0.0000 0.0023 0.0123 0.0616 1.0000 ▇▁▁▁▁
Payrolls 2006 21017 0.0455 0.2981 0.3745 0.0000 0.0093 0.0804 0.5543 1.0000 ▇▁▁▁▂
2007 31020 0.0543 0.1975 0.3016 0.0000 0.0072 0.0443 0.2400 1.0000 ▇▁▁▁▁
2008 44246 0.0958 0.0830 0.1691 0.0000 0.0045 0.0185 0.0715 1.0000 ▇▁▁▁▁

We perform the same operation on the sample of firms retrieved in the previous draft, to see whether we have similar weights on summaries, and we keep the option to use the previous data.

Table 12: Summary Statistics of the within-county weights obtained using firm-level financial variables for the subset of firms used in the previous draft and the balance sheets from 2006, 2007, and 2008.
Within-County Weights Missing Values Complete Rate (%) Mean Std. Dev. Min. Value 25th Median 75th Max. Value Histogram
rank(Current Ratio) 2006 1372 0.9388 0.0191 0.0242 0.0004 0.0042 0.0114 0.0245 0.5000 ▇▁▁▁▁
2007 1764 0.9480 0.0125 0.0157 0.0003 0.0029 0.0075 0.0161 0.5000 ▇▁▁▁▁
2008 2148 0.9578 0.0082 0.0103 0.0002 0.0019 0.0050 0.0105 0.3333 ▇▁▁▁▁
No. Employees 2006 20958 0.0648 0.2171 0.3070 0.0001 0.0096 0.0625 0.2810 1.0000 ▇▁▁▁▁
2007 31216 0.0805 0.1342 0.2399 0.0000 0.0040 0.0235 0.1290 1.0000 ▇▁▁▁▁
2008 45833 0.0995 0.0772 0.1704 0.0000 0.0019 0.0116 0.0577 1.0000 ▇▁▁▁▁
Payrolls 2006 21358 0.0469 0.2902 0.3720 0.0000 0.0075 0.0732 0.5409 1.0000 ▇▁▁▁▂
2007 32063 0.0556 0.1902 0.2952 0.0000 0.0064 0.0414 0.2261 1.0000 ▇▁▁▁▁
2008 46027 0.0956 0.0801 0.1654 0.0000 0.0041 0.0178 0.0684 1.0000 ▇▁▁▁▁

For the entire sample of firms, including those of which we do not have the financial information, we would like to consider the mean of the financial values of the firms in the same size category within their county for which we do have information, and assign it to all firms. However, it is possible that this information covers all firm size categories within a county, it can be that some of the firms in the sample with the bank relationships have no match. We pool together the all the years around 2006, from 2004 and 2008, and we take mean of the financial variables. We calculate the current ratio on the mean value, but we invert it considering 1/current ratio, such that the bigger the number obtained the more severe is the liquidity exposure.

Table 13: Summary Statistics of the within-county weights obtained using mean financial variables at county level pooling financial years from 2004 to 2008.
Within-County Weights Missing Values Complete Rate (%) Mean Std. Dev. Min. Value 25th Median 75th Max. Value Histogram
No. Employees 20330 0.9633 0.0007 0.0020 0.0000 0.0001 0.0002 0.0008 0.1691 ▇▁▁▁▁
Payrolls 20077 0.9637 0.0007 0.0021 0.0000 0.0001 0.0003 0.0007 0.1639 ▇▁▁▁▁
Current Ratio 2608 0.9953 0.0007 0.0009 0.0000 0.0003 0.0005 0.0009 0.1082 ▇▁▁▁▁

There is a significant lower level of missing values for firm weights, but we are not able to cover the full sample in any case.

It would be nice to repeat the same operation for the sample of firms retrieved in the previous draft, but I have not saved the firm size label for the firms thus I am unable to assign the weights to quite some of the firms, dropping down to a sample of 549781 firms when using the financials data to retrieve the size category for the firms.

Measuring County-Level Commerzbank Dependence

In this section, we calculate the measure of Commerzbank dependence at county level using the different weights at firm level we prepared in the previous section, comparing it to the equally weighted measure, the measures calculated from the previous sample of firms, and the one coming from Huber. The following histograms are the result of the computation of county-level Commerzbank dependence using different weighting or applying equal weights at firm level.

Exposure using Equal Weigthing

In the following histogram, we plot the distribution of the county-level Commerzbank dependence using the current firm sample with equal weights - just the arithmetic mean of firm-level Commerzbank dependence within a county - with the distributiuon of the county-level Commerzbank dependence using the previous sample we applied in the last draft. The mean of the distribution is smaller in the current sample (0.079 and 0.083), and we miss one county having less firms. Another important feature to notice for our design is that in the current firms’ sample we have one county with zero county-level Commerzbank dependence, Landau in der Pfalz (KKZ = 07313), and the missing county is Eisenach (KKZ = 16056). The county with zero Commerzbank dependence has two firms, with one bank relationship each and none of them is with Commmerzbank. Instead, in the previous sample we have 459 firms for this county, with an average Commerzbank dependence of 0.049. Overall, they follow a very similar distribution.

Distribution of the county-level Commerzbank dependence with equal   weights (simple county-level average), compared to the measure in the   previous version of the draft.

Figure 6: Distribution of the county-level Commerzbank dependence with equal weights (simple county-level average), compared to the measure in the previous version of the draft.

Exposure using Firm-Level Weights

In the following histograms, we compare the distribution of the county-level Commerzbank dependence using the individual level weights for different closing dates, from 2006 to 2008. Using the individual weights for the current ratio, the distribution is quite different along those years due to survivorship bias. Probably the most reliable source is 2007, following Berg et al. (2021). Comparing this distribution with the equally weighted distribution, the former is quite smoother than the latter but with higher distributional mean.

Distribution of the county-level Commerzbank dependence obtained   using weights from the ranking of current ratio within the county, using   firm-level financial data from 2006, 2007 or 2008.

Figure 7: Distribution of the county-level Commerzbank dependence obtained using weights from the ranking of current ratio within the county, using firm-level financial data from 2006, 2007 or 2008.

Distribution of the county-level Commerzbank dependence obtained   using weights from the ranking of current ratio from 2007 firm-level   financial data within the county, compared to the equal weights.

Figure 8: Distribution of the county-level Commerzbank dependence obtained using weights from the ranking of current ratio from 2007 firm-level financial data within the county, compared to the equal weights.

We repeat the same exercise using individual weights constructed with number of employees and the cost of employees, and we see a very skewed distribution of the county-level Commerzbank dependence towards zero but with higher mean values. Very few firms have complete information about employees. Combining those weights with the weights using the ranking of current ratio does not change the shape of distribution by construction. Using the past data at firm level combined with the available firm level financials used for weighting does not change the distribution significantly.

Distribution of the county-level Commerzbank dependence obtained   using weights from the number of employees within the county, using   firm-level financial data from 2006, 2007 or 2008.

Figure 9: Distribution of the county-level Commerzbank dependence obtained using weights from the number of employees within the county, using firm-level financial data from 2006, 2007 or 2008.

Distribution of the county-level Commerzbank dependence obtained   using weights from the cost of employees within the county, using   firm-level financial data from 2006, 2007 or 2008.

Figure 10: Distribution of the county-level Commerzbank dependence obtained using weights from the cost of employees within the county, using firm-level financial data from 2006, 2007 or 2008.

Exposure using weights from average values at firm size groups

In this section, we analyse the distribution of the county-level Commerzbank exposure obtained by weighting at firm-level within a county assigning the average value of each financial variable we are considering by firm size category at each firm of that firm size. Just to remember, the mean is taken by using the financials from 2004 to 2008 of those firms that are in the sample. Considering the weights for number or cost of employees against the equally weighted exposure, we obtain a smoother distribution and higher in mean, but we drop a few counties where we do not have financials to take the average for each type of firm size in the available data.

Distribution of the county-level Commerzbank dependence obtained   using weights obtained from the average value at firm size level within   a county of the total number of employees and the cost of employees,   compared to the distribution using equal weights.

Figure 11: Distribution of the county-level Commerzbank dependence obtained using weights obtained from the average value at firm size level within a county of the total number of employees and the cost of employees, compared to the distribution using equal weights.

When combining the “size” weights obtained from number and cost of employees at firm size level with the inverse current ratio obtained as mean current liabilities over mean current assets by firm size within a county, we still have a smoother distribution than when using equal weights, but it skews the distribution compared to simply using the average cost of employees by firm size for weighting.

Distribution of the county-level Commerzbank dependence obtained   using weights obtained from the average value at firm size level within   a county of the total number of employees and the cost of employees   combined with the current ratio created from mean total assets and mean   total liabilities by firm size level within a county,   compared to the distribution using equal weights.

Figure 12: Distribution of the county-level Commerzbank dependence obtained using weights obtained from the average value at firm size level within a county of the total number of employees and the cost of employees combined with the current ratio created from mean total assets and mean total liabilities by firm size level within a county, compared to the distribution using equal weights.

The following graphs give an overview of the distribution of county-level Commerzbank dependence for the potentially most relevant and useful weighting techniques among those we have been trying to exploit.

Distribution of county-level Commerzbank dependence, measured using   equal weights (first row), weights using the mean value of firm-level   data of each firm size category pooling balance sheet data from 2004 to   2008 (second row), and combined weights with the inverse current ratio   obtained as the average of current liabilities over the average current   assets of each firm size category. The sample of firms is the newly   obtained from Amadeus, except for the top right histogram, where the   measure is computed using the firm sample from the previous draft.Distribution of county-level Commerzbank dependence, measured using   equal weights (first row), weights using the mean value of firm-level   data of each firm size category pooling balance sheet data from 2004 to   2008 (second row), and combined weights with the inverse current ratio   obtained as the average of current liabilities over the average current   assets of each firm size category. The sample of firms is the newly   obtained from Amadeus, except for the top right histogram, where the   measure is computed using the firm sample from the previous draft.Distribution of county-level Commerzbank dependence, measured using   equal weights (first row), weights using the mean value of firm-level   data of each firm size category pooling balance sheet data from 2004 to   2008 (second row), and combined weights with the inverse current ratio   obtained as the average of current liabilities over the average current   assets of each firm size category. The sample of firms is the newly   obtained from Amadeus, except for the top right histogram, where the   measure is computed using the firm sample from the previous draft.Distribution of county-level Commerzbank dependence, measured using   equal weights (first row), weights using the mean value of firm-level   data of each firm size category pooling balance sheet data from 2004 to   2008 (second row), and combined weights with the inverse current ratio   obtained as the average of current liabilities over the average current   assets of each firm size category. The sample of firms is the newly   obtained from Amadeus, except for the top right histogram, where the   measure is computed using the firm sample from the previous draft.Distribution of county-level Commerzbank dependence, measured using   equal weights (first row), weights using the mean value of firm-level   data of each firm size category pooling balance sheet data from 2004 to   2008 (second row), and combined weights with the inverse current ratio   obtained as the average of current liabilities over the average current   assets of each firm size category. The sample of firms is the newly   obtained from Amadeus, except for the top right histogram, where the   measure is computed using the firm sample from the previous draft.Distribution of county-level Commerzbank dependence, measured using   equal weights (first row), weights using the mean value of firm-level   data of each firm size category pooling balance sheet data from 2004 to   2008 (second row), and combined weights with the inverse current ratio   obtained as the average of current liabilities over the average current   assets of each firm size category. The sample of firms is the newly   obtained from Amadeus, except for the top right histogram, where the   measure is computed using the firm sample from the previous draft.

Figure 13: Distribution of county-level Commerzbank dependence, measured using equal weights (first row), weights using the mean value of firm-level data of each firm size category pooling balance sheet data from 2004 to 2008 (second row), and combined weights with the inverse current ratio obtained as the average of current liabilities over the average current assets of each firm size category. The sample of firms is the newly obtained from Amadeus, except for the top right histogram, where the measure is computed using the firm sample from the previous draft.

Spatial Distribution of Commerzbank Dependence

We acquire the boundaries of the administrative areas (1:250 000) from the Federal Agency for Cartography and Geodesy of Germany (VG250). The dataset includes the administrative units of the hierarchical administrative levels from the country (state) down to the Gemeinden (municipalities) with administrative boundaries, key-numbers, names as well as designations. Lines are SingleLines in sf, whereas areas are MultiPolygons. Each area can comprise several single areas, such as regular area with exclaves or inset areas, each of these multipolygons corresponding to a dataset in the attribute table. The data are structured according to levels (country/state), Länder (federal states), Regierungsbezirke (administrative districts), Kreise (districts/counties), Verwaltungsgemeinschaften (administrative associations), Gemeinden (municipalities), whereby the areas contained are directly carrying the attributive information. Spatial reference is UTM projection in zone 32 or 33 Ellipsoid GRS80, Datum ETRS89 (EPSG:4258).

The dataset is divided into the different administrative levels of Germany:

  • Staat (country) STA
  • Länder (states) LAN
  • Administrative districts RBZ
  • Districts KRS (what we use)
  • Administrative associations VWG
  • Municipalities GEM

Also, in the dataset are comprised:

  • Boundary lines LI, in each case the respective highest level is included.
  • Points PK, each municipality has a point, representing the heart of the municipality.

Data are updated each year with the statuses in 31.12 and 01.01, and we are using the last updated version in September 2021. There are no major changes in the boundaries from 2016. Amtliche Gemeindeschlüssel (AGS) (Official Municipality Key) is the name of what they call KKZ in the SOEP and also present in the Kreise codes matching table, which is derived from the ARS through omission of the administrative association. The hierarchical structure of the administrative levels is represented by the Amtliche Regionalschlüssel (ARS) (territorial code). ARS and AGS constitute the keys of the products of the statistical offices of the Federal Government and of the Länder. Thus, the integration of statistical data and data synchronization, respectively, can easily be performed (cf. also DeStatis).

Details on the attributes:

  • ADE administrative level, we want 4 = District
  • GF geofactor, which can be 1 (waters without structures), 2 (waters with structures), 3 (land without structures), 4 (land with structure). The indication “waters” refers to the North and Baltic Seas as well as to Lake Constance. Each administrative unit has precisely one record entry with the GF value 4.
  • ARS territorial code: 1st-2nd digit for the Land, 3rd digit for the administrative district, 4th-5th digit for the district, 6th-9th digit administrative association, 10th-12th community identification number.
  • AGS official municipality key: 1st-2nd digit is for the Land, 3rd digit for the administrative district, 4th-5th digit for the identification number of the district, and 6th-8th digit for the community identification number.
  • GEN Geographical Name
  • BEZ is the attribute for the district, GEN gives the name, and NBD says whether the attribute should be used for the full name.

We could have actually used this dataset for the identification of firms, but the other table provides us directly with the KKZ codes and not the AGS, which makes it more reliable. I still don’t understand why they are using different names for basically the same codes. For plotting the districts and assign the county values, it is possible to subtract from the shapefile VGS250_KRS everything that is not GF == 4, which is the land with structure, destring the AGS codes and create a new variable kkz_rek from that (they are 401).

Reading layer VG250_KRS' from data source/Users/dubidub/Documents/Projects/CreditPopulism/data/shapes/vg250/VG250_KRS.shp’ using driver `ESRI Shapefile’ Simple feature collection with 431 features and 23 fields Geometry type: MULTIPOLYGON Dimension: XY Bounding box: xmin: 280371.1 ymin: 5235856 xmax: 921292.4 ymax: 6101487 Projected CRS: ETRS89 / UTM zone 32N

Here we plot the spatial distribution of the equally weighted county-level Commerzbank dependence using the current and the past firm sample and the same scale. We also plot the difference in the spatial distribution of the current sample exposure against the past sample. The most striking difference comes from the county that has zero dependence in the current sample, and probably the county for which we have no firms in the current sample. Overall, the current sample calculates smaller values of Commerzbank dependence except for some counties in the south of Germany.

Spatial Distribution of county-level Commerzbank dependence,   measured using equal weights, from the current firm sample (left) and   the past firm sample (right). The district highlighted in red has   zero Commerzbank dependence in the current firm sample. The bins are   deciles from the joint distribution of Commerzbank dependence.Spatial Distribution of county-level Commerzbank dependence,   measured using equal weights, from the current firm sample (left) and   the past firm sample (right). The district highlighted in red has   zero Commerzbank dependence in the current firm sample. The bins are   deciles from the joint distribution of Commerzbank dependence.

Figure 14: Spatial Distribution of county-level Commerzbank dependence, measured using equal weights, from the current firm sample (left) and the past firm sample (right). The district highlighted in red has zero Commerzbank dependence in the current firm sample. The bins are deciles from the joint distribution of Commerzbank dependence.

Difference in Spatial Distribution of county-level Commerzbank   dependence measured using equal weights, current firm sample against   past firm sample.

Figure 15: Difference in Spatial Distribution of county-level Commerzbank dependence measured using equal weights, current firm sample against past firm sample.

We plot the spatial distribution of the county-level Commerzbank dependence calculated by weighting for the average number of employees and the average cost of employees by firm size category within a county. The re-weighting compared to the equal wights should re-balance the measure for firm heterogeneity in size at county level. The variation from equal weights is stronger for areas where bigger firms are more present, for example Ruhr Valley. The variance of the county exposure measure is much higher compared to equally weighting.

Spatial Distribution of county-level Commerzbank   dependence measured using weights obtained from the average number of   employees by firm size within a county, compared against equal weights.

Figure 16: Spatial Distribution of county-level Commerzbank dependence measured using weights obtained from the average number of employees by firm size within a county, compared against equal weights.

Spatial Distribution of county-level Commerzbank   dependence measured using weights obtained from the average cost of   employees by firm size within a county, compared against equal weights.

Figure 17: Spatial Distribution of county-level Commerzbank dependence measured using weights obtained from the average cost of employees by firm size within a county, compared against equal weights.

We plot the spatial distribution of the county-level Commerzbank dependence calculated by weighting for the average number of employees and the average cost of employees by firm size category within a county. The re-weighting compared to the equal wights should re-balance the measure for firm heterogeneity in size at county level. The variation from equal weights is stronger for areas where bigger firms are more present, for example Ruhr Valley. The variance of the county exposure measure is much higher compared to equally weighting.

To conclude, we plot the spatial distribution of the county-level Commerzbank dependence calculated by combining the weighted average of firm-level Commerzbank dependence within a county with weights for the average number of employees (or average cost of employees) by firm size category within the county and the weighted average of firm-level Commerzbank dependence with weights for the inverse current ratio calculated from the average current assets and the average current liabilities by firm size category within the county. The re-weighting procedure should re-balance the importance of a firm within a county for its size and its exposure to a general liquidity shock in the business cycle. Final values are more balanced.

Spatial Distribution of county-level Commerzbank   dependence measured using weights obtained from the average number of   employees combined with weights obtained from the inverse current ratio   from average current assets and average current liabilities   by firm size within a county, compared against equal weights.

Figure 18: Spatial Distribution of county-level Commerzbank dependence measured using weights obtained from the average number of employees combined with weights obtained from the inverse current ratio from average current assets and average current liabilities by firm size within a county, compared against equal weights.

Spatial Distribution of county-level Commerzbank   dependence measured using weights obtained from the average cost of   employees combined with weights obtained from the inverse current ratio   from average current assets and average current liabilities   by firm size within a county, compared against equal weights.

Figure 19: Spatial Distribution of county-level Commerzbank dependence measured using weights obtained from the average cost of employees combined with weights obtained from the inverse current ratio from average current assets and average current liabilities by firm size within a county, compared against equal weights.

References

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